def subset_petab_problem(
petab_problem: petab.Problem,
timecourse_id: str,
) -> Sequence[petab.Problem]:
petab_problem = deepcopy(petab_problem)
petab_problem.observable_df.loc[DUMMY_OBSERVABLE_ID] = \
{
OBSERVABLE_FORMULA: DUMMY_MEASUREMENT,
NOISE_FORMULA: DUMMY_NOISE,
}
# TODO allow no specification of timecourse if only one timecourse in
# problem. TODO raise error if multiple timecourses but no timecourse ID
# specified
petab_problem.measurement_df = petab_problem.measurement_df[
petab_problem.measurement_df[SIMULATION_CONDITION_ID] == timecourse_id]
timecourse = parse_timecourse_string(
petab_problem.timecourse_df.loc[timecourse_id][TIMECOURSE], )
# FIXME timepoints not necessarily float
timecourse = [(float(_t), _id) for _t, _id in timecourse]
petab_problems = []
for index, (timepoint, condition_id) in enumerate(timecourse):
petab_problems.append(deepcopy(petab_problem))
petab_problems[-1].condition_df.loc[timecourse_id] = \
petab_problems[-1].condition_df.loc[condition_id]
# Drop other conditions
# FIXME test how this affects other conditions or how to combine normal conditions + timecourses...
# or only allow timecourses?
petab_problems[-1].condition_df = (
petab_problems[-1].condition_df.loc[[timecourse_id]])
petab_problems[-1].measurement_df = \
petab_problems[-1].measurement_df[
petab_problems[-1].measurement_df[TIME].astype(float)
>= timepoint
]
if index < len(timecourse) - 1:
next_timepoint = timecourse[index + 1][0]
petab_problems[-1].measurement_df = \
petab_problems[-1].measurement_df[
petab_problems[-1].measurement_df[TIME].astype(float)
<= next_timepoint
]
# Add dummy data to ensure endpoint is outputted.
petab_problems[-1].measurement_df = \
petab_problems[-1].measurement_df.append(
{
OBSERVABLE_ID: DUMMY_OBSERVABLE_ID,
SIMULATION_CONDITION_ID: timecourse_id,
TIME: next_timepoint,
MEASUREMENT: DUMMY_MEASUREMENT,
},
ignore_index=True,
)
# Remove condition parameters from the parameters table.
condition_components = [
c for c in petab_problems[-1].condition_df.loc[timecourse_id].index
if c not in NON_COMPONENT_CONDITION_LABELS
]
petab_problems[-1].parameter_df.drop(
condition_components,
inplace=True,
errors='ignore', # only parameters that are in the table are dropped
)
#for t in [timepoint, next_timepoint]:
# if not any(
# t == petab_problems[-1].measurement_df[TIME].astype(float)
# ):
# # FIXME add dummy data
# print(t)
# breakpoint()
# pass
return petab_problems
def filter_observables(petab_problem: petab.Problem):
petab_problem.measurement_df = petab_problem.measurement_df.loc[
petab_problem.measurement_df[petab.OBSERVABLE_ID].apply(
lambda x: x in petab_problem.observable_df.index
), :
]